Sage-Tips

What methods would you use to organize categorical variables?

a) For Categorical Variables: Use Bar chart, pie chart, Pareto chart, side-by-side bar chart to visualize categorical variables. Bar Chart: A bar chart visualizes a categorical variable as a series of bars, with each bar representing the tallies for a single category.

How do you deal with categorical variables with many levels in linear regression?

To deal with categorical variables that have more than two levels, the solution is one-hot encoding. This takes every level of the category (e.g., Dutch, German, Belgian, and other), and turns it into a variable with two levels (yes/no).

How do you handle categorical data with high cardinality?

How handle high cardinality

1. Label Encoder : Replace string values by integer classes [0, 1, 2, 3…]
2. Dummy Encoder : This method consist on creating n new variables of.
3. Aggregating Values : This method consist on aggregating values with low cardinality by creating a “Others” class.

How do you Visualise categorical data?

To visualize a small data set containing multiple categorical (or qualitative) variables, you can create either a bar plot, a balloon plot or a mosaic plot….Visualizing Multivariate Categorical Data

1. Prerequisites.
2. Bar plots of contingency tables.
3. Balloon plot.
4. Mosaic plot.
5. Correspondence analysis.

How do you describe the distribution of a categorical variable?

When a variable is categorical, the number of times each of its values occurs in a set of data is counted. These counts are called frequencies. When a count or frequency is divided by the total count and multiplied by 100, the result is a percentage or percent.

How do you treat categorical variables in machine learning?

Machine learning models require all input and output variables to be numeric. This means that if your data contains categorical data, you must encode it to numbers before you can fit and evaluate a model. The two most popular techniques are an Ordinal Encoding and a One-Hot Encoding.

How do you handle too many categories in categorical variable?

Combine levels: To avoid redundant levels in a categorical variable and to deal with rare levels, we can simply combine the different levels. There are various methods of combining levels. Here are commonly used ones: Using Business Logic: It is one of the most effective method of combining levels.

What is cardinality of categorical variables?

The number of unique categories in a variable is called cardinality.

What plot can we use for categorical variables?

Mosaic plots are good for comaparing two categorical variables, particularly if you have a natural sorting or want to sort by size.

How to avoid redundant levels in a categorical variable?

Combine levels: To avoid redundant levels in a categorical variable and to deal with rare levels, we can simply combine the different levels. There are various methods of combining levels. Here are commonly used ones: Using Business Logic: It is one of the most effective method of combining levels.

How can I fit categorical variables into a regression equation?

You can’t fit categorical variables into a regression equation in their raw form. They must be treated. Most of the algorithms (or ML libraries) produce better result with numerical variable. In python, library “sklearn” requires features in numerical arrays. Look at the below snapshot.

How many possible values can be taken for a categorical variable?

For these categorical variables, you have for example 150 different countries, 50 languages, 50 scientific fields etc… For each categorical variable with many possible value, take only the one having more than 10000 sample that takes this value.

How does the categorical predictors solution work?

The solution is mentioned in classification tree sections. Specifically, the solution orders the levels of the categorical predictor by the number of occurrence of each level in one class, and then treats the predictor as an ordered predictors.